Cereal yield (kg per hectare)

Source: worldbank.org, 01.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 2,253 +7.35% 114
Angola Angola 1,033 +3.32% 158
Albania Albania 5,201 +1.09% 35
United Arab Emirates United Arab Emirates 23,625 +19.3% 3
Argentina Argentina 5,070 -2.37% 38
Armenia Armenia 2,186 +69.8% 116
Antigua & Barbuda Antigua & Barbuda 3,081 +47.1% 92
Australia Australia 2,880 +13.1% 97
Austria Austria 6,896 -3.05% 19
Azerbaijan Azerbaijan 3,163 -4.08% 90
Burundi Burundi 1,484 +0.338% 143
Belgium Belgium 8,604 +8.82% 10
Benin Benin 1,362 -3.34% 149
Burkina Faso Burkina Faso 1,204 +9.89% 150
Bangladesh Bangladesh 5,005 +1.74% 39
Bulgaria Bulgaria 5,151 -13.4% 36
Bahamas Bahamas 7,424 -0.709% 14
Bosnia & Herzegovina Bosnia & Herzegovina 6,913 +56.3% 18
Belarus Belarus 2,971 -1.58% 94
Belize Belize 4,476 +4.35% 53
Bolivia Bolivia 1,717 -32% 131
Brazil Brazil 4,901 +9.46% 43
Barbados Barbados 2,753 -0.228% 101
Brunei Brunei 2,800 +2.44% 99
Bhutan Bhutan 3,449 +3.14% 79
Botswana Botswana 684 -31.8% 171
Central African Republic Central African Republic 830 +2.82% 167
Canada Canada 4,079 +30.7% 63
Switzerland Switzerland 6,084 +10.3% 27
Chile Chile 6,114 -5.81% 25
China China 6,380 +0.996% 22
Côte d’Ivoire Côte d’Ivoire 2,292 +2.43% 112
Cameroon Cameroon 1,694 -1.9% 133
Congo - Kinshasa Congo - Kinshasa 816 -7.17% 168
Congo - Brazzaville Congo - Brazzaville 887 -0.0113% 164
Colombia Colombia 4,342 +1.43% 56
Comoros Comoros 1,752 -0.854% 129
Cape Verde Cape Verde 11.5 -29.4% 178
Costa Rica Costa Rica 3,575 -23.5% 72
Cuba Cuba 2,354 -2.16% 110
Cyprus Cyprus 2,495 +20.9% 104
Czechia Czechia 5,930 -3% 29
Germany Germany 7,126 +1.83% 17
Djibouti Djibouti 2,062 +4.45% 118
Dominica Dominica 1,667 +0.664% 135
Denmark Denmark 7,241 +13.9% 16
Dominican Republic Dominican Republic 5,224 -0.161% 34
Algeria Algeria 1,610 +12.3% 139
Ecuador Ecuador 4,050 +0.879% 64
Egypt Egypt 7,419 +1.09% 15
Eritrea Eritrea 640 +0.0469% 174
Spain Spain 3,307 -21.8% 83
Estonia Estonia 4,225 +20.6% 58
Ethiopia Ethiopia 2,814 -2.7% 98
Finland Finland 3,771 +35.8% 68
Fiji Fiji 4,090 +5.22% 62
France France 6,655 -7.2% 20
Micronesia (Federated States of) Micronesia (Federated States of) 1,713 -0.355% 132
Gabon Gabon 1,587 -0.0126% 141
United Kingdom United Kingdom 7,720 +10.8% 13
Georgia Georgia 2,407 -12.4% 108
Ghana Ghana 2,504 +6.07% 103
Guinea Guinea 1,380 +2.64% 147
Gambia Gambia 930 -12% 163
Guinea-Bissau Guinea-Bissau 1,456 +7.63% 144
Greece Greece 4,143 -1.24% 61
Grenada Grenada 994 -0.0704% 160
Guatemala Guatemala 2,208 +0.629% 115
Guyana Guyana 5,941 +9.15% 28
Hong Kong SAR China Hong Kong SAR China 2,080 +0.478% 117
Honduras Honduras 1,876 +0.262% 126
Croatia Croatia 5,823 -17% 31
Haiti Haiti 1,055 +0.0569% 157
Hungary Hungary 4,032 -31.9% 65
Indonesia Indonesia 5,394 +1.17% 33
India India 3,567 +1.96% 73
Ireland Ireland 8,702 +1.1% 9
Iran Iran 1,827 +11.7% 128
Iraq Iraq 2,902 +6.32% 96
Iceland Iceland 2,967 -4.94% 95
Israel Israel 3,201 -4.54% 89
Italy Italy 4,750 -14.6% 48
Jamaica Jamaica 1,163 +4.03% 153
Jordan Jordan 1,195 -22% 151
Japan Japan 6,273 -7.58% 23
Kazakhstan Kazakhstan 1,377 +31.3% 148
Kenya Kenya 1,449 -2.63% 145
Kyrgyzstan Kyrgyzstan 3,249 +39.2% 87
Cambodia Cambodia 3,654 +2.48% 70
South Korea South Korea 6,603 -3.27% 21
Kuwait Kuwait 13,526 -6.29% 4
Laos Laos 4,454 +7.12% 54
Lebanon Lebanon 2,415 +10.9% 107
Liberia Liberia 1,121 +6.53% 154
Libya Libya 670 +0.0747% 172
Sri Lanka Sri Lanka 3,047 -33.1% 93
Lesotho Lesotho 729 -25.1% 170
Lithuania Lithuania 4,210 +6.93% 59
Luxembourg Luxembourg 6,154 +10% 24
Latvia Latvia 4,186 +7.33% 60
Morocco Morocco 943 -59.5% 162
Moldova Moldova 1,911 -61.4% 123
Madagascar Madagascar 2,793 +4.42% 100
Maldives Maldives 2,436 -0.107% 106
Mexico Mexico 3,981 +2.64% 66
North Macedonia North Macedonia 3,472 -1.86% 76
Mali Mali 1,671 +15.9% 134
Myanmar (Burma) Myanmar (Burma) 3,457 -7.66% 78
Montenegro Montenegro 3,307 +3.56% 84
Mongolia Mongolia 1,113 -21.6% 155
Mozambique Mozambique 1,015 -2.07% 159
Mauritania Mauritania 1,721 -16.3% 130
Mauritius Mauritius 9,247 +7.02% 6
Malawi Malawi 2,009 -13.4% 121
Malaysia Malaysia 3,750 -1.95% 69
Namibia Namibia 649 +31.4% 173
New Caledonia New Caledonia 9,590 +18.6% 5
Niger Niger 557 +54.6% 176
Nigeria Nigeria 1,656 +2.29% 136
Nicaragua Nicaragua 2,359 -1.84% 109
Netherlands Netherlands 8,943 +13.6% 8
Norway Norway 4,728 +13% 49
Nepal Nepal 3,219 +5.07% 88
New Zealand New Zealand 8,522 -3.29% 11
Oman Oman 24,801 +29.1% 2
Pakistan Pakistan 3,406 -2.4% 81
Panama Panama 3,515 -0.00569% 75
Peru Peru 4,635 -1.48% 51
Philippines Philippines 3,822 -0.344% 67
Papua New Guinea Papua New Guinea 4,752 -0.344% 47
Poland Poland 4,862 +6.55% 44
Puerto Rico Puerto Rico 3,278 +10.4% 85
North Korea North Korea 3,517 +1.59% 74
Portugal Portugal 5,137 -4.51% 37
Paraguay Paraguay 4,294 +12.3% 57
Palestinian Territories Palestinian Territories 2,309 +2.52% 111
Qatar Qatar 9,149 +8.51% 7
Romania Romania 3,634 -29.9% 71
Russia Russia 3,429 +26.7% 80
Rwanda Rwanda 1,511 -0.546% 142
Saudi Arabia Saudi Arabia 5,002 +5.81% 40
Sudan Sudan 760 +49.5% 169
Senegal Senegal 1,932 +4.16% 122
Solomon Islands Solomon Islands 1,903 +0.174% 124
Sierra Leone Sierra Leone 1,877 -4.55% 125
El Salvador El Salvador 2,659 -9.86% 102
Somalia Somalia 503 -0.0199% 177
Serbia Serbia 4,638 -19.6% 50
South Sudan South Sudan 1,056 +25% 156
São Tomé & Príncipe São Tomé & Príncipe 2,045 +0.122% 120
Suriname Suriname 4,759 +2.59% 45
Slovakia Slovakia 4,755 -20.5% 46
Slovenia Slovenia 5,565 -18.9% 32
Sweden Sweden 6,107 +20.6% 26
Eswatini Eswatini 1,587 +10.8% 141
Syria Syria 950 +15.8% 161
Chad Chad 846 +3.83% 166
Togo Togo 1,168 +1.59% 152
Thailand Thailand 3,093 +0.999% 91
Tajikistan Tajikistan 3,266 -12% 86
Turkmenistan Turkmenistan 1,875 -10.6% 127
Timor-Leste Timor-Leste 2,046 +70.2% 119
Trinidad & Tobago Trinidad & Tobago 1,638 +3.02% 138
Tunisia Tunisia 1,641 +13% 137
Turkey Turkey 3,465 +18.7% 77
Tanzania Tanzania 1,593 -8.81% 140
Uganda Uganda 2,290 -47.3% 113
Ukraine Ukraine 4,614 -15.4% 52
Uruguay Uruguay 4,345 -21% 55
United States United States 8,072 -2.19% 12
Uzbekistan Uzbekistan 4,975 +5.08% 41
St. Vincent & Grenadines St. Vincent & Grenadines 31,621 +4.01% 1
Venezuela Venezuela 3,384 -3.45% 82
Vietnam Vietnam 5,905 -0.705% 30
Vanuatu Vanuatu 608 -0.181% 175
Yemen Yemen 871 -42.9% 165
South Africa South Africa 4,964 -3.63% 42
Zambia Zambia 2,457 -2.68% 105
Zimbabwe Zimbabwe 1,412 -8.65% 146

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'AG.YLD.CREL.KG'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'AG.YLD.CREL.KG'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))